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SupplementaryMaterial: CARLANE: ALaneDetectionBenchmarkfor UnsupervisedDomainAdaptationfromSimulationto multipleReal-WorldDomains

Neural Information Processing Systems

Does the dataset contain all possible instancesorisitasample(notnecessarilyrandom) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified.


PROSPECT: LabeledTandemMassSpectrometry DatasetforMachineLearninginProteomics

Neural Information Processing Systems

PROSPECT provides value to proteomics and machine learning researchers by including several high-quality annotations and by being accessible in terms of format and structure for applying machinelearning.


LIPS-Learning IndustrialPhysicalSimulation benchmarksuite-Appendix

Neural Information Processing Systems

For each benchmark, we generate three different training datasets. If the dataset is a sample, then what is the larger set? Is the samplerepresentativeofthe larger set(e.g., geographic coverage)? The provided datasets are self-contained and will remain constant. However, more datasets could be generated using the proposed benchmarking platform.



ConfLab: ADataCollectionConcept,Dataset,and BenchmarkforMachineAnalysisofFree-Standing SocialInteractionsintheWild Appendices

Calanir Luthion

Neural Information Processing Systems

Is there anything afuture user could do to mitigate theseundesirableharms? Although ConfLab's long-term vision is towards developing technology to assist individuals in navigating social interactions, the data could also affect a community in unintended ways: for instance, cause worsened social satisfaction, alackofagency,stereotype newcomers andveterans, or benefit only those members of the community who make use of resulting applications at the expense of the rest. More nefarious uses involve exploiting the data for developing methods that harmfully surveilorprofile people.


EPIC-KITCHENSVISORBenchmark VIdeoSegmentationsandObjectRelations-Appendix

Neural Information Processing Systems

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly(i.e.,incombinationwithotherdata)fromthedataset?




Datasheet - SCAMPS

Neural Information Processing Systems

Datasheet for SCAMPS Dataset Synthetics for Camera Measurement of Physiological SignalsMotivationFor what purpose was the dataset created? Was there a specificgap that needed to be filled? CompositionWhat do the instances that comprise the datasetrepresent (e.g., documents, photos, people,countries)? If the dataset is asample, then what is the larger set? However, we created a broadrange of physiological parameters and appearancecharacteristics, which is one of the advantages ofcreating data from a simulation.


MBW: Multi-viewBootstrappingintheWild-SupplementaryMaterial

Neural Information Processing Systems

In this section, we conduct an ablation study analyzing the effects of iterations in our proposed approach. Moreover, we see that as the iterations progress, the 2D landmark prediction error continues to reduce as seen in Figure 1a. The red points represent the frames that were given initial 2D input labels. The colorbar of these scatter plots represents the reprojection error (Eq. Weassume that only asingle object of interest (Chimpanzee in Figure 1is visible in each frame.